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Channel Spatio-Temporal Convolutional Network for Trajectory Prediction

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Ubiquitous Security (UbiSec 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 2034))

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Abstract

Accurate and timely prediction of the future path of agents in the vicinity of an agent is the core of avoiding conflict in automated applications. The traditional method based on RNN model requires high computational cost in the process of prediction, especially for long series prediction. In order to obtain more efficient and accurate prediction trajectory, a channel spatio-temporal convolutional network framework, called CSTCN, is proposed in this paper. The framework models the spatial environment as a block of data input to the CSTCN and captures spatio-temporal interactions using an improved temporal convolutional network. Compared with the traditional model, the spatial and temporal modeling of the proposed model is calculated in each local time window so that it can be executed in parallel to obtain higher computational efficiency. Experimental results on 5 trajectory prediction benchmark datasets demonstrate that the proposed model is superior to other seven state-of-the-art models in both efficiency and accuracy.

This research was funded by the National Natural Science Foundation of China (Grant number 62272006), Natural Science Foundation of Anhui Province (Grant No. 2108085MF214) and the University Collaborative Innovation Project of Anhui Province (grant number GXXT-2022-049).

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Acknowledgements

During the process of writing this paper, I would like to express my special gratitude to Ying Hu for her guidance and supervision, as well as for her understanding and tolerance. Thank you to Professor Yonglong Luo for providing guidance during the model design phase, and to the School of Computer Science and Technology at Anhui Normal University for providing me with a good learning environment.

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Authors and Affiliations

  1. Ahnu Normal University, 189 Jiuhua South Road, Yijiang District, Wuhu City, China

    Zhonghao Lu, Lina Xu, Ying Hu, Liping Sun & Yonglong Luo

Authors
  1. Zhonghao Lu
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  2. Lina Xu
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  3. Ying Hu
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  4. Liping Sun
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  5. Yonglong Luo
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Corresponding author

Correspondence to Yonglong Luo .

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Editors and Affiliations

  1. School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou, China

    Guojun Wang

  2. Department of Computer Science, University of Exeter, Exeter, UK

    Haozhe Wang

  3. Department of Computer Science, University of Exeter, Exeter, UK

    Geyong Min

  4. Applied Research Department, British Telecom, London, UK

    Nektarios Georgalas

  5. Department of Applied Mathematics and Computer Science, University of Denmark, Copenhagen, Denmark

    Weizhi Meng

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Lu, Z., Xu, L., Hu, Y., Sun, L., Luo, Y. (2024). Channel Spatio-Temporal Convolutional Network for Trajectory Prediction. In: Wang, G., Wang, H., Min, G., Georgalas, N., Meng, W. (eds) Ubiquitous Security. UbiSec 2023. Communications in Computer and Information Science, vol 2034. Springer, Singapore. https://doi.org/10.1007/978-981-97-1274-8_14

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  • DOI: https://doi.org/10.1007/978-981-97-1274-8_14

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  • Print ISBN: 978-981-97-1273-1

  • Online ISBN: 978-981-97-1274-8

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